Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams.

Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA's performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies.

Optical Identification of Materials Transformations in Oxide Thin Films.

Recent advances in high-throughput experimentation for combinatorial studies have accelerated the discovery and analysis of materials across a wide range of compositions and synthesis conditions. However, many of the more powerful characterization methods are limited by speed, cost, availability, and/or resolution. To make efficient use of these methods, there is value in developing approaches for identifying critical compositions and conditions to be used as a priori knowledge for follow-up characterization with high-precision techniques, such as micrometer-scale synchrotron-based X-ray diffraction (XRD). Here, we demonstrate the use of optical microscopy and reflectance spectroscopy to identify likely phase-change boundaries in thin film libraries. These methods are used to delineate possible metastable phase boundaries following lateral-gradient laser spike annealing (lg-LSA) of oxide materials. The set of boundaries are then compared with definitive determinations of structural transformations obtained using high-resolution XRD. We demonstrate that the optical methods detect more than 95% of the structural transformations in a composition-gradient La-Mn-O library and a Ga2O3 sample, both subject to an extensive set of lg-LSA anneals. Our results provide quantitative support for the value of optically detected transformations as a priori data to guide subsequent structural characterization, ultimately accelerating and enhancing the efficient implementation of micrometer-resolution XRD experiments.

Fast growth of large-grain and continuous MoS2 films through a self-capping vapor-liquid-solid method.

Most chemical vapor deposition methods for transition metal dichalcogenides use an extremely small amount of precursor to render large single-crystal flakes, which usually causes low coverage of the materials on the substrate. In this study, a self-capping vapor-liquid-solid reaction is proposed to fabricate large-grain, continuous MoS2 films. An intermediate liquid phase-Na2Mo2O7 is formed through a eutectic reaction of MoO3 and NaF, followed by being sulfurized into MoS2. The as-formed MoS2 seeds function as a capping layer that reduces the nucleation density and promotes lateral growth. By tuning the driving force of the reaction, large mono/bilayer (1.1 mm/200 µm) flakes or full-coverage films (with a record-high average grain size of 450 µm) can be grown on centimeter-scale substrates. The field-effect transistors fabricated from the full-coverage films show high mobility (33 and 49 cm2 V-1 s-1 for the mono and bilayer regions) and on/off ratio (1 ~ 5 × 108) across a 1.5 cm × 1.5 cm region.